Image processing device and image processing method

ABSTRACT

In order to provide an image processing device which can select and apply an optimal processing algorithm among a plurality of processing algorithms depending on a part of a processing target image and a processing purpose, reference characteristic curve data is calculated in which pixel values are integrated centered on a centroid of a region of interest for a reference image, and the reference characteristic curve data and a processing algorithm according to a processing purpose are stored in advance in an algorithm table  2  in correlation with each other at least for each part.

TECHNICAL FIELD

The present invention relates to an image processing on a medical imagesuch as a CT image, an MR image, or a US image.

BACKGROUND ART

In the related art, a diagnosis has been performed using a medical imagesuch as a Computed Tomography (CT) image, a Magnetic Resonance (MR)image, or an Ultrasound (US) image. In addition, various imageprocessing methods for extracting an observation target part or removingan unnecessary region from the medical image so as to create an imagesuitable for the diagnosis have been proposed. Further, a computer aideddiagnosis device called Computer Aided Diagnosis (CAD) for detectingabnormal shadows from a medical image has been developed. As above, aplurality of processing algorithms corresponding to various processingpurposes for a medical image have been developed.

However, a medical image shows different features depending on ascanning part or the kind of examination. For this reason, to perform aprocessing in a different process depending on parts achieves goodefficiency even in the same processing purpose. For example, when anabnormal shadow is detected from a medical image, a different abnormalshadow detection algorithm is applied depending on a scanning part ordiagnosis content. For example, the head and the abdomen employdifferent abnormal shadow detection algorithms. As a device forselecting an abnormal shadow detection algorithm appropriate for amedical image, for example, an abnormal shadow detection devicedisclosed in PTL 1 has been proposed.

PTL 1 discloses an abnormal shadow detection device which stores anabnormal shadow detection algorithm for each part of an object, and,when an abnormal shadow is detected from a medical image, creates a setof tomographic images of the anatomically same part which is scanned atdifferent dates and times, obtains a difference between the tomographicimages of each set, identifies an object part of the tomographic image,and selects and applies an abnormal shadow detection algorithm for andto the identified part.

CITATION LIST Patent Literature

-   PTL 1: Japanese Patent No. 4401121

SUMMARY OF INVENTION Technical Problem

However, in the above-described PTL 1, when a scanning part isidentified from a medical image, the scanning part is determined fromcontinuity of a target image (tomographic image) with previous andsubsequent tomographic images, a distribution form of pixels in aspecific pixel value range, or the like. For example, it is determinedwhether or not a scanning part is the head based on whether or not anelliptical region with a high CT value corresponding to the skull ispresent in an image. In addition, it is determined whether or not ascanning part is the neck based on whether or not the part is a regionwhich is continued from head slices. Further, it is determined whetheror not a scanning part is the chest based on whether or not there is ahollow closed region of which a CT value is −900 or less (thespecification, paragraphs [0031] to [0036] of PTL 1).

In addition, in a case where the kind of image (the kind of examination,the kind of scanning method, with or without a contrast medium, and thelike) is different even if a scanning part is the same and a processingpurpose is the same, there is a case where a processing time is reducedby employing a different processing algorithm. For example, since animage obtained without using a contrast medium and an image obtainedusing the contrast medium show different features, a processing may beperformed at high speed by employing a different processing algorithm.

The present invention has been made in consideration of theabove-described problems, and an object thereof is to provide an imageprocessing device capable of selecting and applying the optimalprocessing algorithm according to a part of a processing target imageand a processing purpose among a plurality of processing algorithms.

Solution to Problem

In order to achieve the above-described object, a first inventionrelates to an image processing device which applies a predeterminedprocessing algorithm to a medical image so as to perform an imageprocessing, including a storage unit that calculates referencecharacteristic curve data in which pixel values are computed centered onany point of a region of interest for a reference image, and stores thereference characteristic curve data and a part in correlation with eachother in advance; a calculation unit that calculates targetcharacteristic curve data in which pixel values are computed centered onany point of a region of interest for a processing target image; acomparison unit that compares the target characteristic curve datacalculated by the calculation unit with the reference characteristiccurve data stored in the storage unit; a selection unit that selects aprocessing algorithm stored in correlation with reference characteristiccurve data having high correlation with the target characteristic curvedata from the storage unit on the basis of a result of the comparison bythe comparison unit; and an image processing unit that performs an imageprocessing to which a processing algorithm corresponding to the partselected by the selection unit is applied.

A second invention relates to an image processing method of applying apredetermined processing algorithm to a medical image so as to performan image processing, including a storage step of calculating referencecharacteristic curve data in which pixel values are computed centered onany point of a region of interest for a reference image, and storing thereference characteristic curve data and a part in correlation with eachother in advance; a calculation step of calculating targetcharacteristic curve data in which pixel values are computed centered onany point of a region of interest for a processing target image; acomparison step of comparing the target characteristic curve datacalculated in the calculation step with the reference characteristiccurve data stored in the storage step; a selection step of selecting aprocessing algorithm stored in correlation with reference characteristiccurve data having high correlation with the target characteristic curvedata from the storage step on the basis of a result of the comparison inthe comparison step; and an image processing step of performing an imageprocessing to which a processing algorithm corresponding to the partselected in the selection step is applied.

Advantageous Effects of Invention

According to the present invention, it is possible to provide an imageprocessing device and the like capable of selecting and applying theoptimal processing algorithm according to a part of a processing targetimage and a processing purpose among a plurality of processingalgorithms.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating an overall configuration of an imageprocessing device 100.

FIG. 2 is a diagram illustrating an example of the algorithm table 2stored in the image processing device 100 related to the presentinvention.

FIG. 3 is a flowchart illustrating a flow of the algorithm selectionprocessing.

FIG. 4( a) is a diagram illustrating that on-radius vector pixel valuedata is acquired from a CT image, FIG. 4( b) is a diagram illustratingthat on-radius vector pixel value data is acquired from a binary image,and FIG. 4( a) is a diagram illustrating on-circumference pixel valuedata is acquired from a binary image.

FIG. 5( a) is a diagram illustrating an example of the chest tomographicimage (without contrast medium) 30, FIG. 5( b) is a diagram illustratingan example of the on-radius vector pixel value data 3A which isextracted from a binary image of the chest tomographic image (withoutcontrast medium) 30, and FIG. 5( c) is a diagram illustrating an exampleof the on-circumference pixel value data 3B which is extracted from abinary image of the chest tomographic image (without contrast medium)30.

FIG. 6( a) is a diagram illustrating an example of the chest tomographicimage (with contrast medium) 40, FIG. 6( b) is a diagram illustrating anexample of the on-radius vector pixel value data 4A which is extractedfrom a binary image of the chest tomographic image (with contrastmedium) 40, and FIG. 6( c) is a diagram illustrating an example of theon-circumference pixel value data 4B which is extracted from a binaryimage of the chest tomographic image (without contrast medium) 40.

FIG. 7( a) is a diagram illustrating an example of the upper chest endtomographic image (without contrast medium) 50, FIG. 7( b) is a diagramillustrating an example of the on-radius vector pixel value data 5Awhich is extracted from a binary image of the upper chest endtomographic image (without contrast medium) 50, and FIG. 7( c) is adiagram illustrating an example of the on-circumference pixel value data5B which is extracted from a binary image of the upper chest endtomographic image (without contrast medium) 50.

FIG. 8( a) is a diagram illustrating an example of the abdomentomographic image (with contrast medium) 60, FIG. 8( b) is a diagramillustrating an example of the on-radius vector pixel value data 6Awhich is extracted from a binary image of the abdomen tomographic image(with contrast medium) 60, and FIG. 8( c) is a diagram illustrating anexample of the on-circumference pixel value data 6B which is extractedfrom a binary image of the abdomen tomographic image (with contrastmedium) 60.

FIG. 9 is a diagram illustrating a part presentation image 82 showing apart of a tomographic image.

FIG. 10 is a diagram illustrating an example of the rib extractionalgorithm applied to a chest image with contrast medium.

FIG. 11 is a diagram illustrating the rib extraction algorithm of thechest image with contrast medium.

FIG. 12( a) is a diagram illustrating a chest MIP image 91 before a ribremoval processing is performed, and FIG. 12( b) is a diagramillustrating a chest MIP image 92 after the rib removal processing isperformed.

FIG. 13 is a diagram illustrating an example of the rib extractionalgorithm applied to a chest image without contrast medium.

FIG. 14 is a diagram illustrating an MIP image 93 configured by removinga rib from the chest image without contrast medium.

FIG. 15 is a flowchart illustrating a noise removal processing.

FIG. 16 is a diagram illustrating another example (spiral shape) of thecharacteristic curve data extraction.

FIG. 17 is a diagram illustrating another example (an example in which acenter of a radius vector is set outside an object) of thecharacteristic curve data extraction.

FIG. 18 is a diagram illustrating an example of the algorithm table 200of a second embodiment.

FIG. 19 is a diagram illustrating an example of the referencecharacteristic curve data (on-radius vector pixel value data 311)calculated from an image 211.

FIG. 20 is a diagram illustrating an example of the referencecharacteristic curve data (on-circumference pixel value data 312)calculated from the image 211.

FIG. 21 is a diagram illustrating an example of the referencecharacteristic curve data (x direction addition data 313, and ydirection addition data 314) calculated from the image 211.

FIG. 22 is a schematic diagram of a cross-sectional view 213 of a top ofa head.

FIG. 23 is a schematic diagram of a cross-sectional view 214 of avicinity of eyeballs of the head.

FIG. 24 is a schematic diagram of a cross-sectional view 215 of a chest.

FIG. 25 is a schematic diagram of a cross-sectional view 216 of legs.

FIG. 26 is a flowchart illustrating a flow of an algorithm selectionprocessing of the second embodiment.

DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments of the present invention will be described indetail with reference to the drawings.

First Embodiment

First, the configuration of an image processing system 1 to which animage processing apparatus 100 of the present invention is applied willbe described with reference to FIG. 1.

As shown in FIG. 1, the image processing system 1 includes the imageprocessing device 100 having a display unit 107 and an input unit 109;an image database 111 connected to the image processing device 100through a network 110; and a medical image capturing device 112.

The image processing device 100 is a computer which performsprocessings, such as image generation and image analysis.

As shown in FIG. 1, the image processing device 100 includes a CPU(Central Processing Unit) 101, a main memory 102, a storage unit 103, acommunication interface (communication I/F) 104, a display memory 105,and an interface (I/F) 106 with an external unit such as a mouse 108,and the respective units are connected to each other through a bus 113.

The CPU 101 loads a program stored in the main memory 102 or the storageunit 103 to a work memory region on a RAM of the main memory 102 andexecutes the program and performs driving control of the respectiveunits connected to each other through the bus 113, thereby realizingvarious kinds of processings performed by the image processing device100.

In addition, in the first embodiment, when the CPU 101 performs theintended processing on a processing target image, the CPU executes analgorithm selection processing (FIG. 3) described later, so as to selectand apply the optimal processing algorithm. The algorithm selectionprocessing will be described later.

The main memory 102 is configured to include a ROM (Read Only Memory), aRAM (Random Access Memory), and the like. The ROM permanently holds aboot program of a computer, a program such as BIOS, data, and the like.In addition, the RAM temporarily holds a program, data, and the likeloaded from the ROM, the storage unit 103, and the like, and has a workarea used when the CPU 101 performs various kinds of processings.

The storage unit 103 is a storage unit which performs reading andwriting of data from and in a HDD (hard disk drive) or other recordingmedia. Programs executed by the CPU 101, data required to execute theprograms, an OS (Operating System), and the like are stored in thestorage unit 103. As programs, a control program equivalent to an OS andapplication programs are stored. Each of these program codes is read bythe CPU 101 as necessary and moved to the RAM of the main memory 102,thereby being executed as various kinds of means.

In addition, the storage unit 103 stores an algorithm table 2 shown inFIG. 2.

As shown in FIG. 2, in the algorithm table 2, reference characteristiccurve data items D1, D2, . . . which are calculated from a referenceimage and processing algorithms A1, A2, . . . are registered so to becorrelated with each other. The reference characteristic curve dataitems D1, D2, . . . are calculated at least for the respective parts(position numbers P1, P2, . . . ). Preferably, in addition to the parts,the data is calculated for each kind of image (the kind of examination,the kind of scanning method). As the kind of image, for example, thereis a presence or absence of a contrast medium. This algorithm table 2 isprepared in plurality according to processing purposes.

The reference characteristic curve data items D1, D2, . . . are dataitems which are used as a reference of comparison with targetcharacteristic curve data items in the algorithm selection processingdescribed later.

In addition, reference characteristic curve data items D1, D2, . . . arecharacteristic curves in which a pixel value distribution of atomographic image is computed centered on a centroid of a region ofinterest.

Specifically, the following is calculated as the referencecharacteristic curve data items.

(1) On-radius vector pixel value data in which pixel values ofrespective points on a radius vector A which rotates centered on acentroid of a region of interest are integrated for each rotation angleθ (refer to FIGS. 4( a) and 4(b)).

(2) On-circumference pixel value data in which pixel values ofrespective points on a circumference B centered on a centroid of aregion of interest are integrated for each radius Ri (refer to FIG. 4(c)).

(3) On-ellipse pixel value data in which pixel values of respectivepoints on an circumference of an ellipse centered on a centroid of aregion of interest are integrated for each diameter Re of the ellipse(either the minor axis or the major axis) (not shown).

(4) On-radius vector pixel value data in which pixel values ofrespective points on a radius vector A which rotates centered on acentroid of a region of interest are multiplied by, for example, aweighting factor corresponding to a distance from the centroid so as tobe added with weighting for each rotation angle θ.

(5) On-circumference pixel value data in which pixel values ofrespective points on a circumference B centered on a centroid of aregion of interest are multiplied by, for example, a weighting factorcorresponding to a rotation angle of the radius vector A so as to beadded with weighting for each radius Ri.

In addition, the reference characteristic curve data of a singlerepresentative cross-section may be calculated for each part such as thehead, the neck, the chest, the abdomen, the lumbar region, or the leg,or the reference characteristic curve data of a plurality ofcross-sections may be calculated for each part. When the referencecharacteristic curve data of a plurality of cross-sections for each partis calculated and is registered in the algorithm table 2, a comparisonwith not only a target cross-section but also previous and subsequentcross-sections can be performed upon comparison with targetcharacteristic curve data, and thus it is possible to obtain a moreaccurate comparison result.

In FIG. 2, A1, A2, A3, A4, . . . are identification numbers ofalgorithms, N1, N2, N3, N4, are the number of sampling points of thereference characteristic curve data items D1, D2, D3, D4, . . . , P1,P2, P3, P4, . . . are numbers indicating anatomical positions of imageswhich are bases of the reference characteristic curve data items D1, D2,D3, and D1, D2, D3, D4, . . . are reference characteristic curve dataitems.

The number of sampling points of the reference characteristic curve dataitems D1, D2, D3, D4, . . . are used for normalization. In other words,if the number of sampling points of reference characteristic curve dataand target characteristic curve data is normalized so as to be the same,an accurate comparison can be performed regardless of a physiquedifference between objects.

Each processing algorithm registered in the algorithm table 2 is storedin the storage unit 103.

The communication I/F 104 has a communication control unit, acommunication port, and the like, and mediates communication between theimage processing device 100 and the network 110. In addition, thecommunication I/F 104 performs communication control with the imagedatabase 111, other computers, or the medical image capturing device112, such as an X-ray CT device or an MRI device, through the network110.

The I/F 106 is a port for connection with a peripheral unit and performstransmission and reception of data with the peripheral unit. Forexample, a pointing device, such as the mouse 108 or a stylus pen, maybe connected through the I/F 106.

The display memory 105 is a buffer which temporarily accumulates thedisplay data input from the CPU 101. The accumulated display data isoutput to the display unit 107 at a predetermined timing.

The display unit 107 is formed by a liquid crystal panel, a display unitsuch as a CRT monitor, and a logic circuit which cooperates with thedisplay unit to execute a display processing, and is connected to theCPU 101 through the display memory 105. The display unit 107 displaysthe display data accumulated in the display memory 105 under control ofthe CPU 101.

The input unit 109 is an input unit, such as a keyboard, for example,and outputs to the CPU 101 a variety of instructions or informationinput by the operator. The operator operates the image processing device100 interactively using the display unit 107, the input unit 109, and anexternal unit such as the mouse 108.

In addition, the display unit 107 and the input unit 109 may beintegrally formed such as, for example, a touch panel display. In thiscase, a keyboard array of the input unit 109 is displayed in the touchpanel display.

The network 110 includes various communication networks, such as a LAN(Local Area Network), a WAN (Wide Area Network), an intranet, and theInternet, and mediates communication connection between the imagedatabase 111, a server, or other information devices and the imageprocessing device 100.

The image database 111 accumulates and stores the image data scanned bythe medical image capturing device 112. Although the image processingsystem 1 shown in FIG. 1 has a configuration in which the image database111 is connected to the image processing device 100 through the network110, the image database 111 may be provided, for example, in the storageunit 103 of the image processing device 100.

Next, an operation of the image processing device 100 related to thefirst embodiment will be described with reference to FIGS. 3 to 17.

The CPU 101 of the image processing device 100 reads from the mainmemory 102 a program and data regarding the algorithm selectionprocessing shown in FIG. 2 and executes the processing on the basis ofthe program and data.

In addition, it is assumed that the reference characteristic curve dataitems D1, D2, D3, D4, . . . for a reference image are calculated and areregistered in the algorithm table 2 of the storage unit 103 whenexecution of the following processing starts, as shown in FIG. 2. Inaddition, the processing algorithms A1, A2, A3, A4, . . . which are tobe applied for each part and kind of image (for example, presence orabsence of a contrast medium, or the like) are registered in correlationwith the reference characteristic curve data items D1, D2, D3, D4, . . ..

In addition, it is assumed that data of a computation target image(tomographic image) is received from the image database 111 or the likethrough the network 110 and the communication I/F 104 and is stored inthe storage unit 103 of the image processing device 100.

The image is an image scanned by an X-ray CT device, an MRI device, anultrasonic device, or the like. Although a two-dimensional axial CTimage is used in the following example, a coronal image or a sagittalimage may be used.

The CPU 101 of the image processing device 100 first acquires an image(CT image) which is a processing target, and obtains characteristiccurve data from the acquired image (step S1). Hereinafter, thecharacteristic curve data for the processing target image is referred toas target characteristic curve data.

Here, calculation of the characteristic curve data will be described.

In the first embodiment, a characteristic curve (referencecharacteristic curve data and target characteristic curve data) isobtained by integrating a pixel value distribution of an image, centeredon a centroid of a region of interest.

In addition, although, in the following, a description will be made ofan example in which a region of interest is set to an object region, anda center of the radius vector A or the circumference B is set to acentroid of the object region, a specific organ may be set as a regionof interest such as a backbone region being set as the region ofinterest.

In a case where on-radius vector pixel value data is acquired as thecharacteristic curve, as shown in FIG. 4( a), the CPU 101 sets theradius vector A which rotates centered on a centroid of a region ofinterest on an image 30, and integrates pixel values (CT values in FIG.4( a)) of the respective points on the radius vector A for each rotationangle θ. Then, an integrated value (on-radius vector pixel value data)of the pixel values for the rotation angle θ is obtained as shown inFIGS. 5( b), 6(b), 7(b), 8(b), and the like. FIGS. 5( b), 6(b), 7(b) and8(b) show examples of the on-radius vector pixel value data items 3A,4A, 5A and 6A in which the transverse axis is expressed by θ, and thelongitudinal axis is expressed by an integrated value. Any width of therotation angle θ may be used, and, for example, π/180[rad] may be used.

In addition, as shown in FIG. 4( b), a target pixel may be binarizedusing a predetermined threshold value, the respective pixel values (“1”or “0”) on the radius vector in the binarized tomographic image (abinary image 31) may be integrated so as to calculate on-radius vectorpixel value data. If the on-radius vector pixel value data is calculatedfrom the binary image 31, addition processing is easily performed, andthus the calculation can be performed at high speed.

In addition, in a case where on-circumference pixel value data isacquired as the characteristic curve data, as shown in FIG. 4( c), theCPU 101 sets the circumference B which is centered on a centroid of aregion of interest on the image 30 or the binary image 31, andintegrates pixel values of the respective points on the circumference Bfor each radius Ri. Then, an integrated value (on-circumference pixelvalue data) of the pixel values for the radius Ri is obtained as shownin FIGS. 5( c), 6(c), 7(c), 8(c), and the like. FIGS. 5( c), 6(c), 7(c)and 8(c) show examples of the on-circumference pixel value data items3B, 4B, 5B and 6B in which the transverse axis is expressed by Ri, andthe longitudinal axis is expressed by an integrated value. Any step sizeof the radius Ri may be used, and, for example, one pixel may be used.FIG. 4( c) shows an example in which the respective values (“1” or “0”)of the binary image 31 are integrated; however, on-circumference pixelvalue data may be acquired from a CT image (not shown).

In addition, the circle B of the above-described on-circumference pixelvalue data may be set to an ellipse, and on-ellipse pixel value data maybe acquired as the characteristic curve data. In this case, the CPU 101integrates pixel values of the respective points on a circumference ofan ellipse for each diameter (either the minor axis or the major axis)Re of the ellipse, and generates on-ellipse pixel value data in whichthe transverse axis is expressed by Re, and the longitudinal axis isexpressed by an integrated value. Also for the on-ellipse pixel valuedata, in the same manner as the on-radius vector pixel value data andthe on-circumference pixel value data, either a CT value or a binaryvalue may be used as a computation target, and the characteristic curvedata may be acquired (not shown).

In addition, in a case where CT values are integrated, the CPU 101 mayadd the CT values by being multiplied by a weighting factor depending onthe CT value. For example, if each pixel value is multiplied by areciprocal of the maximum CT value as a weighting factor for all pixels,an additional value can be made to be small, and thus it is possible toprevent an overflow.

In addition, in order to correspond to a physique of an object, a lengthbetween a centroid (a center of the radius vector A) and a body surfaceof the object is preferably normalized, that is, the number of samplingpoints up to the body surface is normalized. Alternatively, in a casewhere the number of sampling points is different, an interpolationcomputation is performed when correlation with a referencecharacteristic curve is computed in step S2 (comparison processing)described later, thereby making the number of sampling points equal.

By normalizing the characteristic curve data in the above-described way,it is possible to perform an accurate comparison even if an object imageis different in a physique from a reference tomographic image which is abase of reference characteristic curve data.

FIGS. 5 to 8 are diagrams illustrating examples of the characteristiccurve data items which are calculated for tomographic images ofdifferent parts.

FIG. 5( a) is a diagram illustrating a contrast medium absent chesttomographic image 30, FIG. 5( b) is a diagram illustrating on-radiusvector pixel value data 3A which is extracted from a binary image of thecontrast medium absent chest tomographic image 30 of FIG. 5( a), andFIG. 5( c) is an example of the on-circumference pixel value data 3Bwhich is extracted from a binary image of the contrast medium absentchest tomographic image 30 of FIG. 5( a).

In addition, FIG. 6( a) is a diagram illustrating a contrast mediumpresent chest tomographic image 40, FIG. 6( b) is a diagram illustratingon-radius vector pixel value data 4A which is extracted from a binaryimage of the contrast medium present chest tomographic image 40 of FIG.6( a), and FIG. 6( c) is a diagram illustrating an example ofon-circumference pixel value data 4B which is extracted from a binaryimage of the contrast medium present chest tomographic image 40 of FIG.6( a).

Further, FIG. 7( a) is a diagram illustrating a contrast medium absentupper chest end tomographic image 50, FIG. 7( b) is a diagramillustrating on-radius vector pixel value data 5A which is extractedfrom a binary image of the contrast medium absent upper chest endtomographic image 50 of FIG. 7( a), and FIG. 7( c) is a diagramillustrating on-circumference pixel value data 5B which is extractedfrom a binary image of the contrast medium absent upper chest endtomographic image 50 of FIG. 7( a).

In addition, FIG. 8( a) is a diagram illustrating an example of acontrast medium present abdomen tomographic image 60, FIG. 8( b) is adiagram illustrating on-radius vector pixel value data 6A which isextracted from a binary image of the contrast medium present abdomentomographic image 60 of FIG. 8( a), and FIG. 8( c) is a diagramillustrating on-circumference pixel value data 6B which is extractedfrom a binary image of the contrast medium present abdomen tomographicimage 60 of FIG. 8( a).

As shown in FIGS. 5 to 8, the on-radius vector pixel value data items 3Ato 6A and the on-circumference pixel value data items 3B to 6B representdifferent curves depending on the parts and the presence or absence ofthe contrast medium. Although not shown, on-ellipse pixel value dataalso represents different curves depending on the parts and the presenceor absence of the contrast medium.

In other words, for example, even in the same chest, characteristiccurves are different depending on the upper chest (apex area) or themiddle chest, and are more greatly different between the chest and theabdomen.

Characteristic curve data of other parts such as the head, the neck, thelumbar region, the leg, and the foot region can be calculated in thesame manner.

In step S1, when target characteristic curve data (at least one of theon-radius vector pixel value data, the on-circumference pixel valuedata, and the on-ellipse pixel value data) is calculated, the CPU 101then compares each reference characteristic curve data item stored inthe storage unit 103 with the target characteristic curve data itemcalculated in step S1 (step S2).

The comparison processing in step S2 is performed, for example, bycalculating the magnitude of correlation between the targetcharacteristic curve data and the reference characteristic curve data.

The magnitude of correlation may be calculated using the Pearsonproduct-moment correlation coefficient represented by the followingEquation (1).

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack & \; \\\begin{matrix}{r = \frac{\left( {{Covariance}\mspace{14mu} {of}\mspace{14mu} {variable}\mspace{14mu} X\mspace{14mu} {and}\mspace{14mu} {variable}\mspace{14mu} Y} \right)}{\begin{pmatrix}{{standard}\mspace{14mu} {deviation}\mspace{14mu} {of}\mspace{14mu} {variable}\mspace{14mu} X \times} \\{{standard}\mspace{14mu} {deviation}\mspace{14mu} {of}\mspace{14mu} {variable}\mspace{14mu} Y}\end{pmatrix}}} \\{= \frac{\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\; {\left( {X_{i} - \overset{\_}{X}} \right)\left( {Y_{i} - \overset{\_}{Y}} \right)}}}{\sqrt{\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\; \left( {X_{i} - \overset{\_}{X}} \right)^{2}}}\sqrt{\frac{1}{n - 1}{\sum\limits_{i = 1}^{n}\; \left( {Y_{i} - \overset{\_}{Y}} \right)^{2}}}}}\end{matrix} & (1)\end{matrix}$

In addition, for simpler comparison, an inter-curve distance between thecharacteristic curve data items is calculated, for example, using eitherone of the following Equations (2) and (3), and the data items with theminimum distance may have the highest correlation.

[Equation 2]

Inter-curve distance=Σ(X _(i) −Y _(i))²  (2)

[Equation 3]

Inter-curve distance=Σabs(X _(i) −Y _(i))  (3)

In addition, in relation to the comparison between the referencecharacteristic curve data and the target characteristic curve data, thesame kind of characteristic curve data may be compared such ascomparison between the on-radius vector pixel value data items(hereinafter, also indicated by A(θ) in some cases), comparison betweenthe on-circumference pixel value data (hereinafter, also indicated byB(R) in some cases), or comparison between the on-ellipse pixel valuedata (hereinafter, also indicated by C(Re) in some cases), or thedifferent kinds of a plurality of characteristic curve data items may becombined into a single curve (for example, A(θ)+B(R)+C(Re)) and may becompared with each other.

In addition, since the backbone is reflected in both the chest image andthe abdomen image and thus is not characteristic, the on-radius vectorpixel value data for this region may not be used. Alternatively, a smallweighting factor WA may be applied to the on-radius vector pixel valuedata for this region, and a curve (WA×A(θ)+WB×B(R)+WC×C(Re)) may becompared into which the on-radius vector pixel value data A(θ), theon-circumference pixel value data B(R), and the on-ellipse pixel valuedata C(Re) are combined.

Next, the CPU 101 selects from the algorithm table 2 a processingalgorithm which is correlated with the reference characteristic curvedata having high correlation with the target characteristic curve dataas a result of the comparison in step S2 (step S3). In other words, aprocessing algorithm is selected which is correlated with referencecharacteristic curve data with the greatest product-moment correlationcoefficient of the above-described Equation (1) or referencecharacteristic curve data with the shortest inter-curve distance of theabove-described Equations (2) and (3).

In addition, as the result of the comparison in step S2, the CPU 101acquires a position number (P1, P2, . . . of the algorithm table 2 ofFIG. 2) correlated with the reference characteristic curve data havingthe highest correlation with the target characteristic curve data, anddisplays a part presentation image 82 which shows a part correspondingto the position number (step S4).

An example of the part presentation image 82 is shown in FIG. 9.

As shown in FIG. 9, an image processing operation screen shown on theleft side of FIG. 9 is assumed to be displayed on a display screen ofthe display unit 107. On the image processing operation screen, an image81 which is a processing target, a “computation” button 83 forinstructing the starting of a computation, a “finish” button 84 forinstructing the finishing of a processing, the above-described partpresentation image 82, and the like are displayed. In addition, althoughnot shown, an operation button or an input column for receiving anoperation such as selection of a processing target or selection of aprocessing purpose may be provided on the image processing operationscreen. For example, in a case where an image processing of extractingbones from a tomographic image of the whole body is performed, when auser selects the “whole body” as a processing target, the CPU 101acquires a series of tomographic images 81 for the whole body from theimage database 111. In addition, when “bone extraction” is selected as aprocessing purpose (not shown), and the “computation” button isoperated, the algorithm selection processing of FIG. 3 starts so as toperform the characteristic curve calculation processing in step S1, thecharacteristic curve comparison processing in step S2, and the algorithmselection processing in step S3. In other words, target characteristiccurve data is calculated in order from the first tomographic image, allthe reference characteristic curve data items D1, D2, . . . stored inthe algorithm table 2 of “bone extraction” which is a processing purposeare compared with the target characteristic curve data, and a processingalgorithm correlated with the reference characteristic curve data havingthe highest correlation is selected. In addition, the CPU 101 obtains apart (for example, the position number “P4”) of the tomographic imageduring the processing on the basis of a result of the comparisonprocessing in step S2, and creates and displays the part presentationimage 82 which clearly indicates a corresponding position 85 on a humanbody image or the like.

In addition, a display processing of the part presentation image 82 instep S4 may be omitted.

Successively, the CPU 101 applies the processing algorithm selected instep S3 to the tomographic image 81 during the processing which is aprocessing target, so as to perform an image processing (step S5).

If it is determined that the target characteristic curve data of thetomographic image 81 is in a condition of “presence of contrast medium”and has high correlation with the reference characteristic curve data ofthe “chest” in the comparison processing of step S2 described above, aprocessing algorithm of FIG. 10 is selected in step S3. In addition, ifit is determined that the target characteristic curve data of thetomographic image 81 is in a condition of “absence of contrast medium”and has high correlation with the reference characteristic curve data ofthe “chest” in the comparison processing of step S2 described above, aprocessing algorithm of FIG. 13 is selected in step S3 and applied.

In a “contrast medium present” rib extraction algorithm of FIG. 10,first, a threshold value processing is performed on a target image (stepS21), and the rib, the calcified region, the backbone, and the highdensity region where a contrast medium is present, are extracted asshown in an image 71 of FIG. 11( a) after the threshold value processingis performed.

Next, the CPU 101 deletes the region with the maximum area in advanceamong the extracted regions (step S22). A processing result is as shownin an image 72 of FIG. 11( b). By deleting the region with the maximumarea in advance, the region which easily contacts with an ellipticalbelt set in the next step is removed in advance.

Further, in order to extract only the rib and the backbone, the CPU 101sets a supplementary FIG. 74 which fits the rib region the best. In theexample of FIG. 11, an elliptical belt as shown in FIG. 11( c) is set asthe supplementary FIG. 74 from position information of the bones at theleft and right ends and position information of the bones at the upperand lower ends (step S23). The CPU 101 extracts the region whichcontacts with the supplementary figure (elliptical belt) 74 as a boneregion (step S24).

By referring to the processing result of the rib extraction processing,a processing of removing the bone region can be performed when aprocessing on other soft tissues or the like is performed. For example,a specific value such as −100 is overwritten on the bone region of theoriginal CT image by using the extracted bone region information, andthereby an image from which the bone region is removed can be generated.For example, FIG. 12( a) is an MIP (maximum intensity projection) image91 of the chest; a specific pixel value (for example, −100 or the like)is overwritten on the bone region of the original CT image (presence ofcontrast medium) by using the bone region information extracted throughthe rib extraction processing of FIG. 10, this is repeatedly performedon tomographic images of the respective cross-sections, and thereby aseries of tomographic images (volume images) from which the bones areremoved is generated. In addition, when an MIP image 92 is configuredusing a series of tomographic images (volume images) from which thebones are removed, the MIP image 92 from which the ribs are removed asshown in FIG. 12( b) is generated.

This is not limited to the MIP image, and may be applied to generationof a three-dimensional image or other processings.

In addition, for example, in the “contrast medium absent” rib extractionalgorithm of FIG. 13, first, a threshold value processing is performedon a target tomographic image (step S31), and the bone region and thecalcified region are extracted as shown in an image 93 of FIG. 14( a)after the threshold value processing is performed.

Next, the CPU 101 sets a supplementary FIG. 74 which fits the rib regionthe best. The supplementary figure is set in the same manner as in theexample of FIG. 7 (step S32). The CPU 101 extracts a region whichcontacts with the supplementary figure (elliptical belt) 74 as the boneregion (step S33).

If a specific value such as −100 is overwritten on the bone region ofthe original CT image by using the extracted bone region informationwith reference to the processing result of the “contrast medium absent”rib extraction processing, an image from which the bone region isremoved can be generated when an image processing on other soft tissuesor the like is performed. For example, a specific pixel value (forexample, −100 or the like) is overwritten on the bone region of theoriginal CT image (absence of contrast medium) by using the bone regioninformation extracted through the rib extraction processing of FIG. 13,this is repeatedly performed on tomographic images of the respectivecross-sections, and thereby a series of tomographic images (volumeimages) from which the bones are removed is generated. In addition, whenan MIP image 93 is configured using a series of tomographic images fromwhich the bones are removed, the MIP image 93 in which the ribs areremoved and the calcified region is drawn as shown in FIG. 14 isgenerated.

In addition to the organ extraction algorithm as shown in FIG. 10 or 13,the processing algorithm may be an organ removal algorithm, an abnormalshadow detection algorithm, an abnormal shadow shape analysis algorithm,an abnormal shadow density analysis algorithm, a noise removalalgorithm, and the like, depending on a processing purpose.

These processing algorithms are all correlated with referencecharacteristic curve data items and are registered in the algorithmtable 2 of corresponding processing purposes.

In addition, these processing algorithms are more preferably preparedfor each kind of image such as a presence or absence of a contrastmedium.

For example, in a case where “noise removal” is selected as a processingpurpose, algorithm selection processing of FIG. 15 is performedreferring to the algorithm table 2 in which a plurality of noise removalalgorithms are registered for the respective parts.

As shown in FIG. 15, first, the CPU 101 extracts characteristic curvedata (at least one of on-radius vector pixel value data,on-circumference pixel value data, and on-ellipse pixel value data) fora processing target image (step S41), and compares the extractedcharacteristic curve data with the respective reference characteristiccurve data items registered in the algorithm table 2 for use in “noiseremoval”, thereby selecting the closest (high correlation) algorithm(step S42). In addition, the selected algorithm is applied to theprocessing target image (step S43). As a result of the comparison, if itis determined that the image is a chest image, a median filter may beemployed as a noise filter, or if it is determined that the image is aleg image, smoothing processing may be performed.

Further, parameters used in each processing algorithm are preferablyregistered in the algorithm table 2 in correlation with the referencecharacteristic curve data for each part, for each kind of image or foreach processing purpose.

For example, in the rib extraction algorithm shown in FIG. 10 or 13, athreshold value used in the threshold value processing (step S21 andstep S31) may be registered in advance depending on a part or a presenceor absence of a contrast medium.

In addition, there is a case where a processing algorithm is the samebut different parameters are used, and, also in this case, parametersused in correlation with the reference characteristic curve data may beregistered in the algorithm table 2 in advance.

As described above, the image processing device 100 of the firstembodiment calculates reference characteristic curve data in which pixelvalues are integrated centered on a centroid of a region of interestwith respect to a reference image, and stores in advance the referencecharacteristic curve data and a processing algorithm according to aprocessing purpose in the algorithm table 2 in correlation with eachother at least for each part. In addition, the CPU 101 calculates targetcharacteristic curve data in which pixel values are integrated centeredon a centroid of a region of interest with respect to a processingimage, compares the calculated target characteristic curve data with thereference characteristic curve data stored in the algorithm table 2,selects from the algorithm table 2 a processing algorithm correlatedwith reference characteristic curve data having the highest correlationwith the target characteristic curve data on the basis of the comparisonresult, and performs an image processing to which the selectedprocessing algorithm is applied.

In addition, the reference characteristic curve data and the targetcharacteristic curve data are set as on-radius vector pixel value datain which pixel values on the radius vector A centered on a centroid of aregion of interest of a tomographic image are added for each rotationangle θ of the radius vector A.

Further, the reference characteristic curve data and the targetcharacteristic curve data may be set as on-circumference pixel valuedata in which pixel values on the circumference B centered on a centroidof a region of interest of a tomographic image are added for each radiusR.

Furthermore, the reference characteristic curve data and the targetcharacteristic curve data may be set as on-ellipse pixel value data inwhich pixel values on a circumference of an ellipse centered on acentroid of a region of interest of a tomographic image are added foreach diameter of the ellipse.

As above, it is possible to specify at least an object part fromcharacteristic curve data indicating a feature of a pixel valuedistribution of an image such as the on-radius vector pixel value data,the on-circumference pixel value data, or the on-ellipse pixel valuedata on the basis of a tomographic image, and to perform imageprocessing by selecting an appropriate processing algorithm. Since aprocessing algorithm appropriate for an image part can be applied, it ispossible to perform processing efficiently, and to thereby perform theprocessing at high speed. In addition, even in a case where an image ofthe whole body is a processing target, an appropriate processingalgorithm can be applied according to a part, and thus image processingcan be performed with high efficiency.

In addition, if a processing algorithm is stored in correlation withreference characteristic curve data, for example, for each kind of imagesuch as a presence or absence of a contrast medium in addition to thepart, it is possible to apply the optimal processing algorithm accordingto the kind of image even in the same part. For this reason, it ispossible to perform processing at higher speed.

In addition, if parameters used in the processing algorithm are alsoregistered in the algorithm table 2 in correlation with the referencecharacteristic curve data, a processing can be performed using optimalparameters and processing algorithm for an image, and thus it ispossible to perform the processing accurately.

In addition, when position information of a processing targettomographic image is acquired based on a comparison result of thereference characteristic curve data and the target characteristic curvedata, and the part presentation image 82 indicating the positioninformation is generated and is displayed, a user can easily confirm aposition of the tomographic image, and thus it is possible to provideoperation circumstances with high convenience.

Further, the above-described characteristic curve data may be obtainedby integrating pixel values (CT values) of a tomographic image, or maybe obtained by integrating pixel values (“1” or “0”) of a binary imagewhich is obtained by binarizing a tomographic image.

In a case of integrating pixel values of the binary image, an additionprocessing is easily performed, and thus processing speed increases.

In addition, in a case of integrating pixel values (CT values), aweighting factor depending on the CT values is applied to the CT valueswhich may be added so as to prevent overflow.

In addition, the number of sampling points is preferably normalized inthe calculated characteristic curve data. Alternatively, computation ispreferably performed by making the number of sampling points equalthrough interpolation when a degree of correlation is computed. Asabove, it is possible to accurately obtain a degree of correlationregardless of a physique of an object or the like by making the numberof sampling points uniform.

Next, a description will be made of another example of characteristiccurve data which is extracted (calculated) from a medical image in thefirst embodiment.

As shown in FIG. 16, characteristic curve data may be arrangement datain which pixel values on a spiral curve D are sampled centered on acentroid of a region of interest. As shown in FIG. 16( a), CT values maybe sampled from a CT image, and, as shown in FIG. 16( b), pixel values(“1” or “0”) of a binary image which is binarized may be sampled. Anexample of the characteristic curve data obtained in FIG. 16( a) is acurve drawn using the transverse axis expressed by a curve length of thespiral curve, and the longitudinal axis expressed by a value which isobtained by multiplying a CT value sampled from the CT image by aweighting factor depending on the CT value. In addition, an example ofthe characteristic curve data obtained from FIG. 16( b) is a curve drawnusing the transverse axis expressed by a curve length of the spiralcurve, and the longitudinal axis expressed by a pixel value of 1 or 0.

As in this example, the characteristic curve data in which the pixelvalues are sampled in a spiral shape is calculated without integratingthe pixel values, and thus the characteristic curve data can beextracted at high speed. In addition, if the number of sampling pointsis to be roughly taken according to the required processing speed, thecharacteristic curve data can be extracted at higher speed.

In addition, an origin of a radius vector, a circumference, or anellipse used when calculating the characteristic curve data may be setoutside a body.

FIG. 17( a) shows an example in which a radius vector E which has anintersection of a straight line contacting with the object lower sideand a straight line contacting with the object left side as an origin isset, and CT values on the radius vector E are integrated for eachrotation angle θ from a CT image 30.

In addition, FIG. 17( b) shows an example in which a radius vector Ewhich has an intersection of a straight line contacting with the objectlower side and a straight line contacting with the object left side asan origin is set, and binary values on the radius vector E areintegrated for each rotation angle θ from a binary image 31.

In addition, FIG. 17( c) shows an example in which a circumference Fwhich has an intersection of a straight line contacting with the objectlower side and a straight line contacting with the object left side asan origin is set, and binary values on the circumference F areintegrated for each radius Ri from the binary image 31.

As above, any position of an origin of a radius vector, a circumference,and an ellipse for extracting characteristic curve data may be used.

In addition, a shape of a radius vector used when calculatingcharacteristic curve data is not required to be limited to a straightline. For example, if a radius vector of a curve bent according to anorgan shape is set, it is possible to obtain characteristic curve datawhich is more characteristic.

Second Embodiment

Next, the image processing device 100 (the image processing system 1)related to the second embodiment will be described.

A hardware configuration of the image processing device 100 of thesecond embodiment is the same as that of the image processing device 100of the first embodiment shown in FIG. 1. The same constituent element asin the image processing device 100 of the first embodiment is given thesame reference numeral, and repeated description will be omitted.

In addition, when the CPU 101 of the image processing device 100 of thesecond embodiment performs intended processing on a processing targetimage (hereinafter, referred to as a processing image), the CPU executesan algorithm selection processing (refer to FIG. 26) described later soas to select and apply the optimal processing algorithm.

In addition, the storage unit 103 of the image processing device 100 ofthe second embodiment stores an algorithm table 200 shown in FIG. 18.

In the algorithm table 200 shown in FIG. 18, reference characteristiccurve data indicating a feature of a reference image and a processingalgorithm according to a processing purpose are stored in correlationwith each other for each part. In addition, an anatomical feature of thereference image and a processing algorithm are stored in correlationwith each other.

The reference image is an image used to extract a feature or as areference of comparison among images of each part and each image type.In the algorithm selection processing, described later, an anatomicalfeature, a characteristic curve (reference characteristic curve data),or the like which is obtained in advance from the reference image iscompared with an anatomical feature or a characteristic curve of theprocessing image, and, as a result, an object part of the processingimage or the kind of image is determined.

As the reference characteristic curve data, the reference characteristiccurve data described in the first embodiment may be used. In otherwords, (1) on-radius vector pixel value data 311 in which pixel valuesof respective points on a radius vector which rotates centered on acentroid of a region of interest of a reference image 211 are integratedfor each rotation angle θ (refer to FIGS. 19), and (2) on-circumferencepixel value data 312 in which pixel values of respective points on acircumference centered on a centroid of a region of interest of thereference image 211 are integrated for each radius Ri (refer to FIG. 20)may be used.

As described in the first embodiment, (3) on-ellipse pixel value data inwhich pixel values of respective points on an circumference of anellipse centered on a centroid of a region of interest are integratedfor each diameter Re of the ellipse (either the minor axis or the majoraxis), (4) on-radius vector pixel value data in which pixel values ofrespective points on a radius vector which rotates centered on acentroid of a region of interest are multiplied by, for example, aweighting factor corresponding to a distance from the centroid so as tobe added with weighting for each rotation angle θ, and (5)on-circumference pixel value data in which pixel values of respectivepoints on a circumference centered on a centroid of a region of interestare multiplied by, for example, a weighting factor corresponding to arotation angle of the radius vector so as to be added with weighting foreach radius Ri, may be used as the above-described referencecharacteristic curve data.

In addition, as shown in FIG. 21, a curve (x direction additional data313) in which pixel values of pixels located in the same line as the xdirection of the reference image 211 are added, a curve (y directionadditional data 314) in which pixel values of pixels located in the sameline as the y direction of the reference image 211 are added, or thelike, may be used as the reference characteristic curve data.

Here, the anatomical feature will be described.

The anatomical feature is a feature of an image which is shown for eachpart in the body axis direction of an object. The part is, for example,the leg, the abdomen, the chest, the shoulder, the neck, the head, orthe like.

From a density distribution of the image, it is possible to recognize anarrangement of the bones, a ratio of the bones to the object, orpresence of an air region. For example, images 213 and 214 shown inFIGS. 22 and 23 are images of the brain, the reference numerals 213 band 214 b indicate the bone region, but there is an anatomical featurein which there is no bone around the center of the brain 213 a (however,there is a case where a certain number of calcified regions are found).In other words, generally, it is possible to determine whether or not animage is a brain image from a ratio of high density regions presentaround the object center.

However, there are cases where, in the head, algorithms applied to animage processing are preferably changed in the top as shown in the image213 of FIG. 22 and the vicinity of the eyeball as shown in the image 214of FIG. 23.

Therefore, even in an image indicating the anatomical feature of thehead, apart is further subdivided, and different processing algorithmsare required to be applied for each part.

In addition, FIG. 24 is an image 215 illustrating a lung field.

As shown in FIG. 24, the lung field has an anatomical feature in whichthere are air regions 215 a and 215 b of which a CT value (pixel value)is small and the area is considerably large inside the object.

Further, FIG. 25 shows an image 216 indicating the legs.

As shown in FIG. 25, the legs have an anatomical feature in which thereare two bone regions 216 a and 216 b which have the approximately samearea in the left and right and a region 216 c where there is no bonebetween the left and right bone regions.

As above, there is a case where apart can be immediately determined fromthe anatomical feature and thus a processing algorithm to be applied canbe decided in a one-to-one relationship.

In addition, there is a case where a different processing algorithm isrequired to be applied when image processing is performed, according toa further subdivided part or the kind of image depending on a part.

In addition, there is a case where an anatomical feature cannot be foundfrom an image.

In the second embodiment, before selecting a processing algorithm usinga characteristic curve, first, in relation to an image of a specificpart in which a processing algorithm may not be changed due to asubdivided part or kind of image, a processing algorithm to be appliedis decided in a one-to-one relationship from an anatomical featurethereof. In addition, in relation to an image of a specific part inwhich a processing algorithm is required to be changed due to asubdivided part or kind of image, a characteristic curve is calculated,correlation between the calculated characteristic curve and a pluralityof specific reference characteristic curve data items decided from ananatomical feature is determined, and a processing algorithm to beapplied is decided based on a result thereof. Further, in relation to animage in which an anatomical feature is not correlated with a processingalgorithm, correlation between a characteristic curve calculated fromthe image and reference characteristic curve data is determined, and aprocessing algorithm to be applied is decided based on a result thereof.

The algorithm table 200 of the second embodiment will be described withreference to FIG. 18.

The algorithm table 200 stores algorithms A21, A22, A23, . . . in whichthe algorithms and the anatomical features are correlated with eachother in a one-to-one relationship, and algorithms An_(—)01, An_(—)02,An_(—)03, . . . in which the algorithms and the anatomical features arecorrelated with each other in a one-to-many (group) relationship, andother algorithms (not correlated with anatomical features) Am, Am+1, . .. .

For example, the algorithms A21, A22, A23, are correlated withanatomical features C21, C22, C23, . . . in a one-to-one relationship.In an algorithm selection processing (refer to FIG. 26) described later,in a case where an anatomical feature extracted from a processing imageis correlated with an algorithm in a one-to-one relationship, thecorrelated processing algorithm is immediately selected and is appliedto an image processing.

In addition, the algorithms An_(—)01, An_(—)02, An_(—)03, An_(—)04,An+1_(—)01, An+1_(—)02, . . . are correlated with the anatomicalfeatures in a one-to-many relationship. In a part (the chest in theexample of FIG. 18) showing the anatomical feature Cn, algorithms to beapplied are different in the upper part and the lower part of a bodyaxis direction, and further, algorithms to be applied are differentdepending on a presence or absence of a contrast medium. For thisreason, the characteristic curves shown in FIGS. 19 to 21 and the likeare used to determine a more accurate part or a presence or absence of acontrast medium from a feature of an image. The reference characteristiccurve data and the algorithm are correlated with each other in aone-to-one relationship. In the algorithm selection processing describedlater, in a case where the anatomical feature Cn extracted from aprocessing image is correlated with an algorithm in a one-to-manyrelationship, correlation between a plurality of referencecharacteristic curve data items D21 to D24 correlated with theanatomical feature Cn and a characteristic curve calculated from theprocessing image is determined so as to decide reference characteristiccurve data with a high degree of correlation, and an algorithmcorrelated with the reference characteristic curve data is selected andis applied.

In addition, the algorithms Am, Am+1, . . . are not correlated with anyanatomical feature. However, the algorithms Am, Am+1, . . . arecorrelated with the reference characteristic curve data items Dm, Dm+1,. . . in a one-to-one relationship.

In the algorithm selection processing, described later, in a case wherea specific anatomical feature cannot be extracted from a processingimage, correlation between a characteristic curve calculated from theprocessing image and the reference characteristic curve data items Dm,Dm+1, . . . which are not correlated with any anatomical feature isdetermined so as to decide reference characteristic curve data with ahigh degree of correlation, and an algorithm correlated with thereference characteristic curve data is selected and is applied.

Next, with reference to FIG. 26, an operation of the image processingdevice 100 of the second embodiment will be described.

The CPU 101 of the image processing device 100 reads from the mainmemory 102 a program and data regarding the algorithm selectionprocessing shown in FIG. 26 and executes the processing on the basis ofthe program and data.

The CPU 101 of the image processing device 100 first acquires an image(CT image) which is a processing target, and obtains an anatomicalfeature from the acquired image (step S51).

Next, the CPU 101 determines whether or not the anatomical featureobtained in step S51 is a specific anatomical feature which iscorrelated with an algorithm in a one-to-one relationship. In otherwords, if the anatomical feature obtained in step S51 is the feature C21(step S52; Yes), the algorithm A21 correlated with the anatomicalfeature C21 in a one-to-one relationship is selected from the algorithmtable 200 (step S53). In addition, if the anatomical feature obtained instep S51 is the feature C22 (step S54; Yes), the algorithm A22correlated with the anatomical feature C22 in a one-to-one relationshipis selected from the algorithm table 200 (step S55), and, if theanatomical feature obtained in step S51 is the feature C23 (step S56),the algorithm A23 correlated with the anatomical feature C23 in aone-to-one relationship is selected from the algorithm table 200 (stepS57).

As above, first, if there is an anatomical feature correlated with analgorithm in a one-to-one relationship, the algorithm is immediatelyselected based on the anatomical feature.

If the anatomical feature obtained in step S51 does not match thespecific anatomical feature correlated with in a one-to-one relationship(No in steps S52, S53 and S54), next, the CPU 101 calculates apredetermined characteristic curve for the processing image (step S58).Hereinafter, the characteristic curve data calculated for the processingtarget image is referred to as target characteristic curve data. Thetarget characteristic curve data calculated here is assumed to becharacteristic curve data of the same kind as the referencecharacteristic curve data stored in the algorithm table 200. Forexample, in a case where the reference characteristic curve data storedin the algorithm table 200 is the on-radius vector pixel value data 311as shown in FIG. 19( b), the same on-radius vector pixel value data 311may also be obtained in step S58.

If the anatomical feature calculated in step S51 is correlated withalgorithms in a one-to-many (group) relationship (in other words,different algorithms are required to be applied depending on asubdivided part or kind of image), first, the CPU 101 determines whatkind of anatomical feature is shown, and further determines correlationbetween the target characteristic curve data calculated in step S58 andthe reference characteristic curve data in the group correlated with thecorresponding anatomical feature so as to select an algorithm.

In other words, if the anatomical feature of the processing imagecalculated in step S51 is the feature Cn (step S59; Yes), correlationbetween a plurality of reference characteristic curve data items D21 toD24 correlated with the anatomical feature Cn and the targetcharacteristic curve data calculated in step S58 is determined (stepS60). In addition, an algorithm correlated with reference characteristiccurve data having a high degree of correlation is selected from thealgorithm table 200 (step S61).

Similarly, if the anatomical feature of the processing image calculatedin step S51 is the feature Cn+1 (step S62; Yes), correlation between aplurality of reference characteristic curve data items D25 and D26correlated with the anatomical feature Cn+1 and the targetcharacteristic curve data calculated in step S58 is determined (stepS63). In addition, an algorithm correlated with reference characteristiccurve data having a high degree of correlation is selected from thealgorithm table 200 (step S61).

If the anatomical feature obtained in step S51 is not correlated withany algorithm (step S62; No), the CPU 101 refers to the algorithm table200, determines correlation between the other reference characteristiccurve data items Dm, Dm+1, . . . (not correlated with any anatomicalfeature) and the target characteristic curve data calculated in stepS58, and selects an algorithm correlated with reference characteristiccurve data having a high degree of correlation from the algorithm table200 (step S64).

In this way, the processing algorithm to be applied to the image isselected.

In addition, in the above-described processing procedures, theprocessings in steps S59 to S62 may not be necessarily provided; and,after an algorithm is selected using an anatomical feature in aone-to-one relationship (steps S51 to S57), a characteristic curve of aprocessing image may be calculated, and correlation between the targetcharacteristic curve data and reference characteristic curve data in thealgorithm table 200 may be determined so as to decide an algorithm.

As described above, the image processing device 100 of the secondembodiment includes the algorithm table 200 which stores an anatomicalfeature of a reference image or reference characteristic curve data anda processing algorithm in correlation with each other. In addition, inthe algorithm selection processing, first, the CPU 101 extracts ananatomical feature from a processing image, and selects from thealgorithm table 200 a processing algorithm which is correlated with theextracted anatomical feature in a one-to-one relationship. In a casewhere the extracted anatomical feature is not correlated with aprocessing algorithm in a one-to-one relationship, a characteristiccurve indicating a feature of the image is extracted from the processingimage, reference characteristic curve data having high correlation withthe calculated characteristic curve is decided, and a processingalgorithm correlated with the decided reference characteristic curvedata is selected from the algorithm table 200.

Therefore, in a case where a processing algorithm to be applied can beimmediately decided from an anatomical feature, a processing such ascalculation of a characteristic curve of the processing image ordetermination of correlation between target characteristic curve dataand reference characteristic curve data can be omitted, and thuscomputation can be performed at high speed.

Further, in a case where a part specified from an anatomical feature issubdivided into a plurality of parts or is subdivided into a pluralityof kinds of images, the reference characteristic curve data items aregrouped for each subdivided part or each kind of image and are stored inthe algorithm table 200 in correlation with a corresponding anatomicalfeature. In the algorithm selection processing, in a case where ananatomical feature extracted from the processing image is not correlatedwith a processing algorithm in a one-to-one relationship, first, aplurality of reference characteristic curve data items included in agroup corresponding to the anatomical feature are read from thealgorithm table 200, reference characteristic curve data having highcorrelation with the characteristic curve is decided among a pluralityof read reference characteristic curve data items, and a processingalgorithm correlated with the decided reference characteristic curvedata is selected from the algorithm table 200.

Since a plurality of reference characteristic curve data items includedin a group corresponding to an anatomical feature are defined in advancein the algorithm table 200, a reference characteristic curve forperforming correlation determination can be restricted, and thus it ispossible to omit a wasteful correlation determination processing and tothereby perform computation at higher speed.

As above, although the preferred embodiments of the image processingdevice related to the present invention have been described withreference to the accompanying drawings, the present invention is notlimited to these examples. It is obvious that a person skilled in theart can conceive of a variety of modifications or alterations within thescope of the technical spirit disclosed in the present application, andit is understood that they are also naturally included in the technicalscope of the present invention.

REFERENCE SIGNS LIST

1 Image processing system, 100 Image processing device, 101 CPU, 102Main memory, 103 Storage unit, 104 Communication I/F, 105 Displaymemory, 106 I/F, 107 Display unit, 108 Mouse, 109 Input unit, 2Algorithm table, 30 Chest tomographic image (without contrast medium),40 Chest tomographic image (presence of contrast medium), 50 Upper chestend tomographic image (without contrast medium), 60 Abdomen tomographicimage (with contrast medium), 3A, 4A, 5A and 6A On-radius vector pixelvalue data, 3B, 4B, 5B and 6B On-circumference pixel value data, 82 Partpresentation image, 91 MIP image before rib removal processing isperformed, configured from chest image with contrast medium, 92 MIPimage after rib removal processing is performed, configured from chestimage with contrast medium, 93 MIP image after rib removal processing isperformed, configured from chest image without contrast medium, A Radiusvector for extracting characteristic curve data, B Circumference forextracting characteristic curve data, C Curve (spiral shape) forextracting characteristic curve data, E Radius vector for extractingcharacteristic curve data (origin is located outside body), FCircumference for extracting characteristic curve data (origin islocated outside body), 200 Algorithm table, 211 to 215 Image, 311On-radius vector pixel value data, 312 On-circumference pixel valuedata, 313 x direction additional data, 314 y direction additional data

1. An image processing device which applies a predetermined processingalgorithm to a medical image so as to perform an image processing,comprising: a storage unit that calculates reference characteristiccurve data in which pixel values are computed centered on any point of aregion of interest for a reference image, and stores the referencecharacteristic curve data and a part in correlation with each other inadvance; a calculation unit that calculates target characteristic curvedata in which pixel values are computed centered on any point of aregion of interest for a processing target image; a comparison unit thatcompares the target characteristic curve data calculated by thecalculation unit with the reference characteristic curve data stored inthe storage unit; a selection unit that selects a part stored incorrelation with reference characteristic curve data having highcorrelation with the target characteristic curve data from the storageunit on the basis of a result of the comparison by the comparison unit;and an image processing unit that performs image processing to which aprocessing algorithm corresponding to the part selected by the selectionunit is applied.
 2. The image processing device according to claim 1,wherein the reference characteristic curve data and the targetcharacteristic curve data are on-radius vector pixel value data in whichpixel values on a radius vector centered on a centroid of a region ofinterest of the image are added for each rotation angle.
 3. The imageprocessing device according to claim 1, wherein the referencecharacteristic curve data and the target characteristic curve data areon-circumference pixel value data in which pixel values on acircumference centered on a centroid of a region of interest of theimage are added for each radius.
 4. The image processing deviceaccording to claim 1, wherein the reference characteristic curve dataand the target characteristic curve data are on-ellipse pixel value datain which pixel values on a circumference of an ellipse centered on acentroid of a region of interest of the image are added for eachdiameter of the ellipse.
 5. The image processing device according toclaim 1, wherein the reference characteristic curve data is acquireddepending on presence or absence of a contrast medium, and is stored inthe storage unit in correlation with a processing algorithm for eachpart and each kind of image.
 6. The image processing device according toclaim 1, wherein the storage unit further stores a parameter used in theprocessing algorithm in correlation with the reference characteristiccurve data.
 7. The image processing device according to claim 1, furthercomprising: a display unit that acquires position information of theprocessing target image on the basis of the comparison result by thecomparison unit, and displays apart presentation image indicating theposition information.
 8. The image processing device according to claim1, wherein the comparison unit compares target characteristic curve datawith reference characteristic curve data for a series of images ofcross-section positions including a target image.
 9. The imageprocessing device according to claim 1, wherein the comparison unitobtains the correlation on the basis of an inter-curve distance betweenthe reference characteristic curve data and the target characteristiccurve data.
 10. The image processing device according to claim 1,wherein the reference characteristic curve data and the targetcharacteristic curve data are calculated by adding pixel values of thetarget image with weighting or by integrating pixel values of a binaryimage which is obtained by binarizing the image.
 11. The imageprocessing device according to claim 1, wherein the storage unit furtherstores an anatomical feature of the reference image and the processingalgorithm in correlation with each other, and wherein the selection unitextracts the anatomical feature from the processing target image beforeperforming selection based on the comparison result, and selects theprocessing algorithm which is correlated with the extracted anatomicalfeature in a one-to-one relationship from the storage unit.
 12. Theimage processing device according to claim 11, wherein, when a partspecified from the anatomical feature is further subdivided into aplurality of parts or a plurality of kinds of images, the storage unitgroups the reference characteristic curve data items for each subdividedpart or each kind of image so as to be stored in correlation with thecorresponding anatomical feature, and wherein, when the processingalgorithm is not correlated with the anatomical feature extracted fromthe processing target image in a one-to-one relationship, first, theselection unit reads a plurality of reference characteristic curve dataitems included in a group corresponding to the anatomical feature fromthe storage unit, decides the reference characteristic curve data havinghigh correlation with the target characteristic curve data among theplurality of read reference characteristic curve data items, and selectsthe processing algorithm correlated with the decided referencecharacteristic curve data from the storage unit.
 13. The imageprocessing device according to claim 11, wherein the anatomical featureis any one of a ratio of bone regions present around an object center, aratio of a lung field region to the area of the object, and presence oftwo bone regions and presence of a region where there is no bone betweenthe two bone regions.
 14. An image processing method of applying apredetermined processing algorithm to a medical image so as to performan image processing, comprising: a storage step of calculating referencecharacteristic curve data in which pixel values are computed centered onany point of a region of interest for a reference image, and storing thereference characteristic curve data and a part in correlation with eachother in advance; a calculation step of calculating targetcharacteristic curve data in which pixel values are computed centered onany point of a region of interest for a processing target image; acomparison step of comparing the target characteristic curve datacalculated in the calculation step with the reference characteristiccurve data stored in the storage step; a selection step of selecting aprocessing algorithm stored in correlation with reference characteristiccurve data having high correlation with the target characteristic curvedata from the storage step on the basis of a result of the comparison inthe comparison step; and an image processing step of performing an imageprocessing to which a processing algorithm corresponding to the partselected in the selection step is applied.